FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training

• URL: http://arxiv.org/abs/2509.02521v2
• Date: Thu, 11 Sep 2025 13:07:17 GMT
• Title: FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training
• Abstract summary: Full dialog models aim to speak simultaneously, delivering rapid responses.<n>To help address this issue, we introduce natural monologues, which are composed of continuous sentences and waiting intervals.<n>We find a proper training paradigm critical for semantically aligning natural monologues with audio.<n>A dual training paradigm alternates the position of the monologues across different training stages.<n>As confirmed by experimental results, FLM-AudioAudio achieves different superior response qualities and chatting experiences.
• Score: 38.868221563879366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Full-duplex dialog models aim to listen and speak simultaneously, delivering rapid responses to dynamic user input. Among different solutions to full duplexity, a native solution merges multiple channels in each time step, achieving the lowest latency. However, prevailing designs break down the textual monologue sentences for word-level alignment with audio streams, which degrades language modeling abilities. To help address this issue, we introduce natural monologues, which are composed by continuous sentences and waiting intervals, mimicking humanoid cognitive behavior in dialogs. We find a proper training paradigm to be critical for semantically aligning natural monologues with audio. To this end, we develop a dual training paradigm that alternates the position of the monologues, either leading or trailing the audio, across different training stages. A combination of our natural monologue and dual training strategy is applied in developing FLM-Audio, our 7B spoken dialog chatbot with native full-duplexity. As confirmed by experimental results, FLM-Audio achieves superior response qualities and chatting experiences while requiring significantly less training data.

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